Turn Punjabi audio
в text you can trust.

A different approach to Punjabi transcription.

Punjabi transcription is the process of using speech recognition, natural language processing, and machine learning to turn spoken Punjabi, in Gurmukhi or transliterated form, into accurate written text. Punjabi is spoken by more than 125 million people worldwide, across Punjab, Pakistan, and diaspora communities in the UK, Canada, and beyond, and teams doing research, sales, media, or community work with Punjabi speakers have needed a reliable way to get spoken Punjabi into text for years.

Why manual Punjabi transcription breaks down

For most teams, transcribing Punjabi by hand means replaying a recording two or three times to catch a phrase, second-guessing a word that lands differently in a Doabi or Majhi dialect, and losing track of who said what after the fifth interview. Punjabi is also written in two scripts, Gurmukhi in India and Shahmukhi in Pakistan, so a transcript that ignores which one your speakers actually use is only half useful. Generic transcription tools built for English stumble on Punjabi entirely, or return a wall of text with no speaker labels, no structure, and no way to search it later.

Reading Punjabi the way a fluent listener would

Speak AI transcribes spoken Punjabi in your language, with 100+ supported, and returns clean Gurmukhi text with speaker labels, timestamps, and structured fields your systems can use. Recordings can also be translated into English or another language inside the same workspace, so a Punjabi interview and its English summary live in one searchable file instead of two disconnected documents.

Then the questions start. Ask across your entire Punjabi transcript history with AI chat, using ChatGPT, Claude, and Gemini built in, instead of manually rereading every file.

What teams ask their Punjabi recordings

• “What did interviewees say about pricing, in their own words?”
• “Which Punjabi interviews mention a competitor by name?”
• “Summarize the main themes across all Punjabi focus groups this quarter.”
• “Which calls sound frustrated, and which promised follow-ups have we missed?”
• “Translate every mention of scheduling into English.”

From a stack of files to a searchable archive

The result is a Punjabi transcription workflow that scales past one bilingual staffer with headphones. Interviews, sales calls, and community recordings become searchable text within minutes, and dashboards you can customize and white-label track themes, sentiment, and volume over time, so this quarter’s recordings are measured against last quarter’s. One multilingual education provider put its assessments and interviews through Speak AI’s embedded recorders and scaled multilingual assessment across every classroom, without adding headcount.

And because Punjabi recordings rarely live alone, the same engine connects transcription to the MCP server, so every file is queryable from the AI assistants your team already runs.